An Efficient Acyclic Contact Planner for Multiped Robots

Abstract

We present a contact planner for complex legged locomotion tasks: standing up, climbing stairs using a handrail, crossing rubble and getting out of a car. The need for such a planner was shown at the Darpa Robotics Challenge, where such behaviors could not be demonstrated (except for egress). Current planners suffer from their prohibitive algorithmic complexity, because they deploy a tree of robot configurations projected in contact with the environment. We tackle this issue by introducing a reduction property: the reachability condition. This condition defines a geometric approximation of the contact manifold, which is of low dimension, presents a Cartesian topology, and can be efficiently sampled and explored. The hard contact planning problem can then be decomposed into two sub-problems: first, we plan a path for the root without considering the whole-body configuration, using a sampling-based algorithm; then, we generate a discrete sequence of whole-body configurations in static equilibrium along this path, using a deterministic contact-selection algorithm. The reduction breaks the algorithm complexity encountered in previous works, resulting in the first interactive implementation of a contact planner (open source). While no contact planner has yet been proposed with theoretical completeness, we empirically show the interest of our framework: in a few seconds, with high success rates, we generate complex contact plans for various scenarios and two robots, HRP-2 and HyQ. These plans are validated either in dynamic simulations, or on the real HRP-2 robot.

Differential Dynamic Programming for Multi-Phase Rigid Contact Dynamics

“…All the motions were computed offline. Contact sequence [25] and the centroidal trajectory [30] are precomputed and provided to the solver for the large stride experiments. We used the standard controller OpenHRP [31] for tracking the motions on the real robot.…”

Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

“…Policy Learning. Modeling contact interactions and multicontact planning still result in complex optimization problems [51,52,64] that remain sensitive to inaccurate actuation and state estimation. We formulate contact-rich manipulation as a model-free reinforcement learning problem to investigate its performance when relying on multimodal feedback and when acting under uncertainty (such as uncertain geometry, clearance, and configuration for our peg insertion task).…”

Contact Planning for the ANYmal Quadruped Robot Using an Acyclic Reachability-Based Planner

“…Reinforcement learning has shown more and more impressive results during the last few years [17,18] but, as of yet, the results are limited to either flat ground or to behaviors that are unsuitable for real robot hardware on other terrains. In this paper, we apply the work of Steve Tonneau et al [19] on our robotic platform, ANYmal [20], and explain some of the adjustments that need to be done to successfully compute feasible contact plans.…”

“…First, an algorithm analyses the environment to extract the set of possible contact surfaces. The planning problem is then decomposed into two subproblems, as described in [19]; the algorithm searches for a trajectory of the main body of the robot, then contacts are created along this trajectory. This decomposition allows for a considerable reduction in problem complexity, as after the trajectory for the main body is found each limb is considered separately.…”